Decreasing readmissions through better discharge planning (DP) and transitional care is a national healthcare priority. RightCare Solutions has leveraged over 10 years of interdisciplinary academic research led by a nurse researcher, and through our highly successful phase one SBIR grant demonstrated market value for the D2S2 product and the technical expertise of our team. The D2S2 is a six-item decision support tool installed by RightCare Solutions in the hospital EHR to assist discharge planners to identify high-risk patients upon admission allowing time and focus to target appropriate transitional and post-acute care to prevent readmissions. We have achieved outstanding outcomes from our phase 1 award and are proposing further technological developments to enhance our commercial launch. The market potential for the D2S2 tool is significant since discharge decision support is estimated to be applicable to roughly 6,500 U.S. hospitals with a census that is 60% older adults equaling 14 million discharges per year. The phase 1 results indicate a significant impact on 30 and 60 day readmissions giving us strong evidence as to the value of the product. However, the results and our experience using the software indicate there is opportunity to enhance the product's accruacy and functionality. We propose to enhance our predictive accuracy through innovative data mining and machine learning techniques and to improve the functionality by electronically connecting the acute and post-acute care settings. Due to implementation in the three hospitals of the University of Pennsylvania Health System we have data on over 6,000 patients and through the continued live implementation we will accumulate data on over 25,000 patients by the start of the phase 2 grant. Using existing, and new data generated from continued operations, this proposed SBIR grant will advance the product in two major ways to enhance the commercial benefit to its users. Aim 1: Develop, test, and scale SMART capabilities, a dynamic process for improving prediction accuracy, by using hospital-specific and patient level characteristics (D2S2 variables and additional clinical and non-clinical characteristics) and modern data mining/machine learning techniques. AIM 2: Operationalize the D2S2 recommendations by electronically connecting high-risk patients with post-acute care (PAC) providers and stakeholders, known as CONNECT capabilities. Called a learning health system the end- product of this SBIR grant will provide continuous evaluation and improvement to the end-user measured against their goals. Our innovative design produces a closed-loop system that will use data from the D2S2 and hospital databases over time to get smarter. Further, our enhanced product will link acute care to post-acute providers giving them advanced warning of patients who will shortly come to them for care.